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swarms
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feat: add gpu utilization

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pull/282/head^2
Zack 1 year ago
parent 287eb8526f
commit 61acbfff8a
5 changed files with 115 additions and 14 deletions
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0
__init__.py
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6
app.py
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@ -327,10 +327,6 @@ with gr.Blocks() as demo:
buttonChat = gr.Button("Chat")
memory_utilization = gr.Slider(label="Memory Utilization:", min=0, max=1, step=0.1, default=0.5)
iface = gr.Interface(
fn=download_model,
inputs=["text", memory_utilization],
)
chatbot = gr.Chatbot(show_label=False, visible=True).style(height=600)
buttonClear = gr.Button("Clear History")
@ -341,7 +337,7 @@ with gr.Blocks() as demo:
with gr.Column(scale=1):
model_url = gr.Textbox(label="VLLM Model URL:", placeholder="URL to download VLLM model from Hugging Face", type="text");
buttonDownload = gr.Button("Download Model");
buttonDownload.click(fn=download_model, inputs=[model_url]);
buttonDownload.click(fn=download_model, inputs=[model_url, memory_utilization]);
model_chosen = gr.Dropdown(
list(available_models), value=DEFAULTMODEL, multiselect=False, label="Model provided",
info="Choose the model to solve your question, Default means ChatGPT."

2
flagged/log.csv
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@ -0,0 +1,2 @@
model_url,Memory Utilization:,output,flag,username,timestamp
facebook/opt-125m,8,,,,2023-12-05 23:01:52.301896
1 model_url Memory Utilization: output flag username timestamp
2 facebook/opt-125m 8 2023-12-05 23:01:52.301896

114
swarms/app.py
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@ -1,14 +1,65 @@
import os
import time
from functools import partial
from pathlib import Path
from threading import Lock
import warnings
from swarms.modelui.modules.block_requests import OpenMonkeyPatch, RequestBlocker
from swarms.modelui.modules.logging_colors import logger
from vllm import LLM
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ['BITSANDBYTES_NOWELCOME'] = '1'
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
warnings.filterwarnings('ignore', category=UserWarning, message='Using the update method is deprecated')
warnings.filterwarnings('ignore', category=UserWarning, message='Field "model_name" has conflict')
with RequestBlocker():
import gradio as gr
import matplotlib
matplotlib.use('Agg') # This fixes LaTeX rendering on some systems
import swarms.modelui.modules.extensions as extensions_module
from swarms.modelui.modules import (
chat,
shared,
training,
ui,
ui_chat,
ui_default,
ui_file_saving,
ui_model_menu,
ui_notebook,
ui_parameters,
ui_session,
utils
)
from swarms.modelui.modules.extensions import apply_extensions
from swarms.modelui.modules.LoRA import add_lora_to_model
from swarms.modelui.modules.models import load_model
from swarms.modelui.modules.models_settings import (
get_fallback_settings,
get_model_metadata,
update_model_parameters
)
from swarms.modelui.modules.utils import gradio
import gradio as gr
from swarms.tools.tools_controller import MTQuestionAnswerer, load_valid_tools
from swarms.tools.singletool import STQuestionAnswerer
from langchain.schema import AgentFinish
import os
import requests
from swarms.modelui.server import create_interface
from tool_server import run_tool_server
from threading import Thread
from multiprocessing import Process
import time
from langchain.llms import VLLM
tool_server_flag = False
def start_tool_server():
@ -72,6 +123,29 @@ return_msg = []
chat_history = ""
MAX_SLEEP_TIME = 40
def download_model(model_url: str, memory_utilization: int):
# Extract model name from the URL
model_name = model_url.split('/')[-1]
# TODO continue debugging
# response = requests.get(model_url, stream=True)
# total_size = int(response.headers.get('content-length', 0))
# block_size = 1024 #1 Kibibyte
# progress_bar = gr.outputs.Progress_Bar(total_size)
# model_data = b""
# for data in response.iter_content(block_size):
# model_data += data
# progress_bar.update(len(data))
# yield progress_bar
# Save the model data to a file, or load it into a model here
vllm_model = LLM(
model=model_url,
trust_remote_code=True,
gpu_memory_utilization=memory_utilization,
)
available_models.append((model_name, vllm_model))
return gr.update(choices=available_models)
def load_tools():
global valid_tools_info
global all_tools_list
@ -82,7 +156,7 @@ def load_tools():
all_tools_list = sorted(list(valid_tools_info.keys()))
return gr.update(choices=all_tools_list)
def set_environ(OPENAI_API_KEY: str,
def set_environ(OPENAI_API_KEY: str = "",
WOLFRAMALPH_APP_ID: str = "",
WEATHER_API_KEYS: str = "",
BING_SUBSCRIPT_KEY: str = "",
@ -96,7 +170,8 @@ def set_environ(OPENAI_API_KEY: str,
STEAMSHIP_API_KEY: str = "",
HUGGINGFACE_API_KEY: str = "",
AMADEUS_ID: str = "",
AMADEUS_KEY: str = "",):
AMADEUS_KEY: str = "",
VLLM_MODEL_URL: str = ""):
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["WOLFRAMALPH_APP_ID"] = WOLFRAMALPH_APP_ID
os.environ["WEATHER_API_KEYS"] = WEATHER_API_KEYS
@ -206,10 +281,19 @@ def clear_history():
chat_history = ""
yield gr.update(visible=True, value=return_msg)
title = 'Swarm Models'
# css/js strings
css = ui.css
js = ui.js
css += apply_extensions('css')
js += apply_extensions('js')
# with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as demo:
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=14):
gr.Markdown("<h1 align='left'> Swarm Tools </h1>")
gr.Markdown("")
with gr.Column(scale=1):
gr.Image(show_label=False, show_download_button=False, value="images/swarmslogobanner.png")
@ -242,14 +326,25 @@ with gr.Blocks() as demo:
with gr.Column(scale=0.15, min_width=0):
buttonChat = gr.Button("Chat")
memory_utilization = gr.Slider(label="Memory Utilization:", min=0, max=1, step=0.1, default=0.5)
iface = gr.Interface(
fn=download_model,
inputs=["text", memory_utilization],
)
chatbot = gr.Chatbot(show_label=False, visible=True).style(height=600)
buttonClear = gr.Button("Clear History")
buttonStop = gr.Button("Stop", visible=False)
with gr.Column(scale=1):
model_chosen = gr.Dropdown(
list(available_models), value=DEFAULTMODEL, multiselect=False, label="Model provided",
info="Choose the model to solve your question, Default means ChatGPT."
with gr.Column(scale=4):
with gr.Row():
with gr.Column(scale=.5):
model_url = gr.Textbox(label="VLLM Model URL:", placeholder="URL to download VLLM model from Hugging Face", type="text");
buttonDownload = gr.Button("Download Model");
buttonDownload.click(fn=download_model, inputs=[model_url]);
model_chosen = gr.Dropdown(
list(available_models), value=DEFAULTMODEL, multiselect=False, label="Model provided",
info="Choose the model to solve your question, Default means ChatGPT."
)
with gr.Row():
tools_search = gr.Textbox(
@ -297,3 +392,6 @@ with gr.Blocks() as demo:
# demo.queue().launch(share=False, inbrowser=True, server_name="127.0.0.1", server_port=7001)
demo.queue().launch()

7
tests/tools/toolui.py
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@ -1,9 +1,14 @@
import sys
from unittest import result
import gradio_client
import pytest
import os
from unittest.mock import patch, MagicMock
from app import set_environ, load_tools, download_model
from swarms.app import set_environ, load_tools, download_model
current_dir = os.path.dirname(os.path.abspath(__file__))
# Add the parent directory (the root of your project) to the Python path
sys.path.insert(0, os.path.join(current_dir, '..'))
def test_set_environ():

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